Harvey Introduces Harvey Tenet: A Kimi K3 Base Post-Trained with Fireworks for Long-Horizon Legal Agent Work

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Harvey has released Harvey Tenet, its first post-trained model, as a research preview as of today. Tenet is a Kimi K3 base post-trained with Fireworks through asynchronous reinforcement learning on long-horizon legal work. The training corpus combined synthetic data, publicly available legal data, and human expert data. Harvey states no customer data was used. Against the base K3 model, Tenet completes almost twice as many held-out tasks on Harvey’s Legal Agent Benchmark (LAB) and 20% more on LAB: Contracts, raising all-pass rate by 9 and 2 percentage points respectively. Harvey reports state-of-the-art on LAB: Contracts and second place on LAB. The gains also transferred, untrained, to Mercor’s APEX Agents and Crosby’s Redline Bench. The stated goal is twofold: build frontier legal intelligence on open-weight models, and give law firms a path to own their own specialized models.

Is it deployable?

Not yet, Harvey Tenet is a research preview announced on August 20, 2026. Harvey has not published weights, a model card, or an API endpoint. The base model is open-weight; Tenet itself is Harvey’s own checkpoint, and the company says the work will move “from research to production” inside Harvey’s products over time. What ships today is the recipe, not the artifact.

Company tier: Enterprise only. Access runs through Harvey’s platform, which is sold to law firms, mid-sized firms, and in-house legal teams. A lab with an RL stack could reproduce the method; training used roughly 150 NVIDIA B300 GPUs over two months.

Industries: Legal services, corporate in-house legal, private …

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• Originalquelle / Hersteller-Advisory lesen: https://www.marktechpost.com/2026/08/23/harvey-tenet-post-trained-kimi-k3-legal-agent-model/
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